An efficient and low complex model for optimal RBM features with weighted score-based ensemble multi-disease prediction. Issue 3 (17th February 2023)

• Record Type: Journal Article
• Authors: Anish, T. P.; Joe Prathap, P. M.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 26:Issue 3(2023)
• Page Start: 350
• Page End: 372
• Publication Date: 2023-02-17
• Subjects: Multi-disease prediction -- deep belief network -- deviation-based hybrid grasshopper barnacles mating optimization -- deep neural network -- extreme learning machine -- long short term memory -- ensemble learning -- optimized weighted score and thresholding; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2022.2129969
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: Multi-disease prediction is regarded as the capacity to simultaneously identify various diseases that are expected to be affected an individual at a certain period. These multiple diseases are seemed to be at various progression levels and need to be detected in the patient at the time of clinical visits. Diverse studies in the literature have included the predictive models for particular diseases yet, it is unable to notice humans with multiple diseases since humans are mostly suffered not only from a single disease but also from multiple diseases. Hence, this article aims to implement a novel multi-disease prediction model using an ensemble learning approach with deep features. The required data for the multi-disease prediction is collected from the standard datasets. Then, the collected data are given into the "Deep Belief Network (DBN)" approach, where the features are obtained from the RBM layers. These RBM features are tuned with the help of Deviation-based Hybrid Grasshopper Barnacles Mating Optimization (D-HGBMO) for improving the prediction performance. The optimized RBM features are considered in the ensemble learning model named Ensemble, in which the multi-disease prediction is performed with "Deep Neural Network (DNN), Extreme Learning Machine (ELM), and Long Short Term Memory." The predicted score from three classifiers is used in the optimized weighted score and thresholding-based final prediction using the same D-HGBMO for determining the accurate multi-disease prediction results. The experimental results show the effective performance of the proposed model by comparing it with the existing classifiers with the help of different quantitative measures.